A new distance measure for model-based sequence clustering.
Where this comes from
- Record sourced from PubMed, PMID 19443928.
- Also identified by DOI 10.1109/TPAMI.2008.268.
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Abstract
We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in real-world scenarios, a model selection scheme is also proposed.
Medical subject headings
- Algorithms
- Artificial Intelligence
- Information Storage and Retrieval
- Pattern Recognition, Automated
- Sequence Analysis